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                <span class="date">2019-05-20</span>
            
            
            
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          <p><a href="https://blog.csdn.net/u013630349/article/details/47301869" target="_blank">Python与MATLAB 矩阵操作小结</a></p><hr id="null"><p>If you are interested in this topic.﻿<br></p><p>You can get in touch with me.﻿</p><p>18234056952(Tel  wechat  qq)</p>
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          <p>画出流程图，并写出相应的Python代码：<br>!!!一定注意括号用英文的(), 引号为英文的"", 逗号为英文的,!!!<br><br>#01：输入一个数，输出他的后一位。<br>#02：输入一个数，分别输出他的前三位。 <br>#03：输入一个不为0的数，判断它的正负性，并输出"正"或"负"。 <br>#04：分别输入两个数，输出他们的和。 <br>#05：分别输入两个数，判断他们的和的正负性，并输出"正"或"负"。 <br>#06：分别输入两个数，判断他们的和的正负性，若它们的和&gt;0：输出"正"；否则分别输出这两个数的前1位。 <br>#07：输入一个数a，若a&gt;0, 输出“此数为正数”，否则输出该数的后1位。<br>#08：输入一个数b，若b&gt;=0, 分别输出该数的后3位，否则输出“此数为负数”。<br>#09：输入一个数c，若c&gt;0, 分别输出该数的后3位，否则输出该数的后1位。<br>#10：分别输入两个数a,b, 判断他们的和“c”的正负性，若c&gt;0, 输出“两数和为正”，否则分别输出该两数的后1位。<br>#11：分别输入两个数a,b, 判断他们的和“c”的正负性，若c&gt;=0, 分别输出该两数的前1位，否则输出“两数和为负”。<br>#12：输入某同学成绩a，判断是否及格，输出“及格”（a&gt;=60），“不及格"<br>#13：输入某货车总重量a，判断是否超载(a&gt;=2000), 输出“超载”或“正常”。<br>#14：输入某同学成绩a，判断“优”(a&gt;=85)，“及格”（a&gt;=60），“不及格"，并输出。</p>
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          <hr id="null"><p><a href="https://www.jianshu.com/p/87e82a42aec1" target="_blank">差值算法 输入ui界面</a><br></p><p>十分钟带你入门<a href="https://mp.weixin.qq.com/s?__biz=MzI5NDY1MjQzNA==&amp;mid=2247489265&amp;idx=4&amp;sn=3613ea2e55a85f2320a9454d3cb56f0e&amp;chksm=ec5ec98cdb29409a04d09f11ba90dc2f5ef31421102817b1cf5b95326cbac16f7400d636b934&amp;xtrack=1&amp;scene=0&amp;subscene=131&amp;clicktime=1555207951&amp;ascene=7&amp;devicetype=android-27&amp;version=2700033d&amp;nettype=cmnet&amp;abtest_cookie=BAABAAoACwASABMABQAmlx4AVpkeAMyZHgDSmR4A3JkeAAAA&amp;lang=zh_CN&amp;pass_ticket=tLTCHX0y4yiersoPkcaUCl2RN3pr6p7w9sJ92zO6QlF03XQtmZiLJR931w3SqpA%2F&amp;wx_header=1" target="_blank">最python风格的Gui</a></p><p><a href="https://www.jianshu.com/p/70ee420cf4de" target="_blank">数据查询及可视化系统</a></p><p><a href="https://www.jianshu.com/p/3fecc758402a" target="_blank">13个gui库</a></p><hr id="null"><p>If you are interested in this topic.﻿<br></p><p>You can get in touch with me.﻿</p><p>18234056952(Tel  wechat  qq)</p>
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            <figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br></pre></td><td class="code"><pre><span class="line">import json</span><br><span class="line">import pymysql</span><br><span class="line">import requests</span><br><span class="line">from bs4 import BeautifulSoup</span><br><span class="line">from requests.exceptions import RequestException</span><br><span class="line">headers = &#123;</span><br><span class="line">    &apos;User-Agent&apos;:&apos;Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36&apos;,</span><br><span class="line">    &apos;Host&apos;:&apos;www.lagou.com&apos;,</span><br><span class="line">    &apos;Referer&apos;:&apos;https://www.lagou.com/jobs/list_%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90?px=default&amp;city=%E6%88%90%E9%83%BD&apos;,</span><br><span class="line">    &apos;X-Anit-Forge-Code&apos;:&apos;0&apos;,</span><br><span class="line">    &apos;X-Anit-Forge-Token&apos;:&apos;None&apos;,</span><br><span class="line">    &apos;X-Requested-With&apos;:&apos;XMLHttpRequest&apos;,</span><br><span class="line">    &apos;Origin&apos;:&apos;https://www.lagou.com&apos;</span><br><span class="line">    &#125;</span><br><span class="line">data = &#123;</span><br><span class="line">    &apos;first&apos;:&apos;true&apos;,</span><br><span class="line">    &apos;pn&apos;:&apos;1&apos;,</span><br><span class="line">    &apos;kd&apos;:&apos;数据分析&apos;</span><br><span class="line">&#125;</span><br><span class="line">def get_one_page(url):</span><br><span class="line">    try:</span><br><span class="line">        respones = requests.post(url, headers=headers, data=data)</span><br><span class="line">        respones.encoding = &apos;utf-8&apos;</span><br><span class="line">        if respones.status_code == 200:</span><br><span class="line">            return respones.text</span><br><span class="line">        else:</span><br><span class="line">            print(&quot;访问异常，状态码：&quot;+ respones.status_code)</span><br><span class="line">    except RequestException as e:</span><br><span class="line">        print(&apos;请求错误&apos;)</span><br><span class="line">        print(e)</span><br><span class="line">        return None</span><br><span class="line">def main():</span><br><span class="line">    url = &apos;https://www.lagou.com/jobs/positionAjax.json?px=default&amp;city=%E6%88%90%E9%83%BD&amp;needAddtionalResult=false&apos;</span><br><span class="line">    html = get_one_page(url)</span><br><span class="line">    print(html)</span><br><span class="line"># if main() == &apos;__main()__&apos;:</span><br><span class="line">#    main()</span><br><span class="line">#我写的错代码，很奔溃……</span><br><span class="line">if __name__ == &apos;__main__&apos;:</span><br><span class="line">    main()</span><br></pre></td></tr></table></figure>
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          <p><a href="https://blog.csdn.net/daodaipsrensheng/article/details/81408212" target="_blank">梯度下降是 步长为欧式范数的 最速下降</a></p><p><a href="http://www.cnblogs.com/happylion/p/4172632.html" target="_blank">梯度下降是泰勒1阶展开，牛顿法是二阶展开</a></p><hr id="null"><h1>原理:</h1><p>f(X)在任意一点X0处泰勒展开</p><h1>公式:</h1><p>X(k+1)=Xk－α f'(Xk)</p><p>X1=X0－α  f'(X0)</p><h1>几何意义:</h1><p>令X0朝向梯度的负方向走α步,得到新的X1.</p><p>不断的循环上一步，就会走到极小值点。</p><hr id="null"><h1>
<a href="https://blog.csdn.net/UESTC_C2_403/article/details/74910107" target="_blank">三种梯度下降算法</a>的<a href="https://www.jianshu.com/p/aa40508e3cc9" target="_blank">Python实现</a>:</h1><p><a href="https://blog.csdn.net/qq_41080850/article/details/85391267" target="_blank">小批量梯度下降</a>(Mini Batch Gradient Descent)</p><hr id="null"><h1><a href="https://www.jianshu.com/p/cc50de4061c7?utm_campaign=haruki&amp;utm_content=note&amp;utm_medium=reader_share&amp;utm_source=weixin&amp;from=singlemessage" target="_blank">实例之二分类</a></h1><hr id="null"><p>If you are interested in this topic.﻿<br></p><p>You can get in touch with me.﻿</p><p>18234056952(Tel  wechat  qq)</p>
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          <p>```</p><p></p><p>#首先了解如何快速做一个列表﻿</p><p></p><p>df4 = pd.DataFrame({'col1':['1',3],'col2':[2,4]},index=['a','b'])﻿</p><p>#接下来我们来看如何处理脏数据﻿</p><p>import pandas as pd﻿</p><p>import numpy as np﻿</p><p>df=pd.DataFrame({"id":["1","2","3,4"]})﻿</p><p>df﻿﻿<br></p><p>def max_str(t):﻿</p><p>    a=[int(i) for i in t]﻿</p><p>    return max(a)﻿</p><p>df["id_max"]=df["id"].str.split(",").map(max_str)﻿</p><p>df﻿﻿<br></p><p>#腾讯广告算法大赛为例子﻿</p><p>##################################制作小样本集##############################﻿</p><p>###########################################################################﻿</p><p>#第一步 读取数据﻿</p><p>df=pd.read_table('C:/Users/fafa/Desktop/testA/user_data',sep = '\t',header=None,engine='python')﻿</p><p>#增加 列名﻿</p><p>df.columns=['用户ID','年龄','性别','地域','婚恋状态','学历','消费能力','设备','工作状态','连接类型','行为性趣']﻿</p><p>#切分数据，使用前2000条﻿</p><p>df2=df.head(2000)﻿</p><p>#导出切分好的数据﻿</p><p>df2.to_excel('C:/Users/fafa/Desktop/testa/user_data.xls')﻿﻿<br></p><p>##################同理，制作 其余的小样本集##########﻿</p><p>test=pd.read_table('C:/Users/fafa/Desktop/testA/test_sample.dat',sep = '\t',header=None,engine='python')﻿</p><p>test.columns=['样本id','广告id','创建时间','素材尺寸','广告行业id','商品类型','商品id','广告账户id','投放时段','人群定向','出价(单位分)']﻿</p><p>test.to_excel('C:/Users/fafa/Desktop/testa/测试数据.xls')﻿﻿<br></p><p>test=pd.read_table('C:/Users/fafa/Desktop/testA/ad_operation.dat',sep = '\t',header=None,engine='python')﻿</p><p>test.columns=['广告id','创建/修改时间','操作类型','修改字段','操作后的字段']﻿</p><p>df3=test.head(2000)﻿</p><p>df3.to_excel('C:/Users/fafa/Desktop/testa/广告操作数据.xls')﻿﻿<br></p><p>df4=pd.read_table('C:/Users/fafa/Desktop/testA/ad_static_feature.out',sep = '\t',header=None,engine='python')﻿</p><p>df4.columns=['广告id','创建时间','广告账户id','商品id','商品类型','广告行业id','素材尺寸']﻿</p><p>df5=df4.head(2000)﻿</p><p>df5.to_excel('C:/Users/fafa/Desktop/testa/广告静态数据.xls')﻿﻿<br></p><p>df=pd.read_table('C:/Users/fafa/Desktop/testA/想',sep = '\t',header=None,engine='python')﻿﻿<br></p><p>df.columns=['广告请求id','广告请求时间','广告位id','用户id','曝光广告id','曝光广告素材尺寸','曝光广告出价bid','曝光广告pctr','曝光广告quality_ecpm','曝光广告totalEcpm']﻿﻿<br></p><p>df=df.head(2000)﻿﻿<br></p><p>df.to_excel('C:/Users/fafa/Desktop/广告曝光日志.xls')﻿﻿<br></p><p>###################################################﻿</p><p>###################################################﻿</p><p>##########统计日志中广告id的出现次数，并关联其信息##########﻿</p><p>###################################################﻿</p><p>###################################################﻿</p><p>#读取曝光日志﻿</p><p>df=pd.read_excel('D:/mini数据集/曝光日志.xls',header=0)﻿</p><p>#对"姓名" 计数，得到 “姓名”和“计数”两列。﻿</p><p>df.姓名.value_counts().to_frame().reset_index().rename({"index":"姓名","姓名":"计数"},axis=1)﻿﻿<br></p><p>#对"曝光广告id" 计数，得到 “广告id”和“曝光次数”两列。﻿</p><p>df1=df.曝光广告id.value_counts().to_frame().reset_index().rename({"index":"广告id","曝光广告id":"曝光次数"},axis=1)df1.head()﻿</p><p>#法二 ﻿</p><p># df['count'] = 1 ﻿</p><p>#df.groupby('Name')['count'].agg('sum')﻿﻿<br></p><p>#关联广告其他属性 到 曝光次数 表格﻿</p><p>#读取广告其他属性df2﻿</p><p>df2=pd.read_excel('D:/mini数据集/广告静态数据.xls',header=0)df2.head()﻿</p><p>#关联﻿</p><p>df1.merge(df2,on="广告id")﻿</p><p>#删除不需要的列﻿</p><p>df4=df3.drop({'创建时间',"广告账户id"},1)df4.head()﻿</p><p>#修改列的位置﻿</p><p>df=df[['广告id', '商品id', '商品类型', '广告行业id', '素材尺寸','曝光次数']]﻿﻿<br></p><p>#################################################﻿</p><p>###########以下是直接调取大形数据制作训练集#########﻿</p><p>#################################################﻿</p><p>#第一步 读取数据df1=pd.read_table('C:/Users/fafa/Desktop/testA/totalExposureLog.out',sep = '\t',header=None,engine='python')df2=pd.read_table('C:/Users/fafa/Desktop/testA/ad_static_feature.out',sep = '\t',header=None,engine='python')﻿</p><p>#增加 列名﻿</p><p>df1.columns=['广告请求id','广告请求时间','广告位id','用户id','曝光广告id','曝光广告素材尺寸','曝光广告出价bid','曝光广告pctr','曝光广告quality_ecpm','曝光广告totalEcpm']df2.columns=['广告id','创建时间','广告账户id','商品id','商品类型','广告行业id','素材尺寸']#对"曝光广告id" 计数，得到 “广告id”和“曝光次数”两列。 ﻿</p><p>df3=df1.曝光广告id.value_counts().to_frame().reset_index().rename({"index":"广告id","曝光广告id":"曝光次数"},axis=1) df1.head() ﻿</p><p>#关联广告其他属性 到 曝光次数 表格 ﻿</p><p>df3.merge(df2,on="广告id") ﻿</p><p>#删除不需要的列 ﻿</p><p>df4=df3.drop({'创建时间',"广告账户id"},1) df4.head() ﻿</p><p>#修改列的位置 ﻿</p><p>df5=df4[['广告id', '商品id', '商品类型', '广告行业id', '素材尺寸','曝光次数']] ﻿</p><p>#发现id中存在脏数据 所以清理一下（方法见脏数据的清理）﻿</p><p>def max_str(t):﻿</p><p>    a=[int(i) for i in t] ﻿</p><p>    return max(a)﻿</p><p>df5["广告id"]=df5["广告id"].str.split(",").map(max_str)﻿</p><p>df5["商品id"]=df5["商品id"].str.split(",").map(max_str)﻿</p><p>df5["广告行业id"]=df5["广告行业id"].str.split(",").map(max_str)﻿</p><p>#令空值NaN为0﻿</p><p>df5.fillna(0)﻿</p><p>df5.head()﻿﻿<br></p><p>###最终得到的df5就是一个数据集合，最后一列是Y，其余列都是特征X（要注意这里df5是有列名的）。然后套用nn模板(或者Light gbm 代码在此)来训练即可。﻿﻿<br></p><p>####nn模板如下####﻿</p><p># -*- coding: utf-8 -*-﻿</p><p>import pandas as pd﻿</p><p>import numpy as np﻿</p><p>from keras import metrics﻿</p><p>from keras.models import Sequential﻿</p><p>from keras.layers import Dense﻿</p><p>from keras.wrappers.scikit_learn import KerasClassifier﻿</p><p>from sklearn.model_selection import KFold, cross_val_scoredataset=pd.read_csv('housing.csv',header=None)﻿</p><p>X=dataset.iloc[:,0:13]﻿</p><p>Y=dataset.iloc[:,13]﻿</p><p># print(Y)﻿</p><p>seed=7﻿</p><p>np.random.seed(seed)﻿</p><p># 建立模型﻿</p><p>optimizer='adam'﻿</p><p>init='normal'﻿</p><p>model=Sequential()﻿</p><p>model.add(Dense(units=13,activation='relu',input_dim=13,kernel_initializer=init))﻿</p><p>#构建更多的隐藏层﻿</p><p>model.add(Dense(units=10,activation='relu',kernel_initializer=init))﻿</p><p>model.add(Dense(units=1,kernel_initializer=init)) ﻿</p><p>#输出层不需要进行激活函数,预测回归的话unit=1# 编译模型﻿</p><p>model.compile(loss='mse',optimizer=optimizer,metrics=['acc'])﻿</p><p>model.fit(X.values,Y.values,epochs=30,batch_size=64)﻿﻿<br></p><p>```</p><div class="image-package">
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</div><p><a href="https://zhuanlan.zhihu.com/p/34634023" target="_blank">数据分析基本过程</a></p><p><a href="https://blog.csdn.net/qq_41776781/article/details/89873430" target="_blank">XGBOOST模型训练数据集</a><br></p><hr id="null"><p>If you are interested in this topic.﻿<br>You can get in touch with me.<br>18234056952(Tel  wechat  qq)</p>
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            <p>数据库<a href="https://mp.weixin.qq.com/s?__biz=MzU1Nzg0OTQ1Mg==&amp;mid=2247485083&amp;idx=1&amp;sn=d79bd9c901094cc303a60ee9a360125c&amp;chksm=fc2ec898cb59418e16c1e15910f16eb0b603cd6697e309a50fb9a2c6fc2c14378147ffa01419&amp;xtrack=1&amp;scene=0&amp;subscene=131&amp;clicktime=1557922296&amp;ascene=7&amp;devicetype=android-28&amp;version=2700043a&amp;nettype=cmnet&amp;abtest_cookie=BAABAAoACwASABMABQAjlx4AVpkeAMyZHgDcmR4A8ZkeAAAA&amp;lang=zh_CN&amp;pass_ticket=AVoMJJlByi8lnWeYhEerxyDnG%2BraR3Nh5QRSuVCEqOhlV06OpV%2BL8wX%2FH9VhLevp&amp;wx_header=1" target="_blank" rel="noopener">mongoDB</a><br>MySQL<a href="https://mp.weixin.qq.com/s?__biz=MzU1Nzg0OTQ1Mg==&amp;mid=2247485093&amp;idx=1&amp;sn=70698e661f7f4b29d7b2b3af0368ff8f&amp;chksm=fc2ec8a6cb5941b026c7239afd1cbfe45711e588d1849cccf31b27e2da69d0f91ae451b390e3&amp;xtrack=1&amp;scene=0&amp;subscene=131&amp;clicktime=1558006312&amp;ascene=7&amp;devicetype=android-28&amp;version=2700043a&amp;nettype=cmnet&amp;abtest_cookie=BAABAAoACwASABMABgAjlx4AVpkeAMyZHgDcmR4A8ZkeAAOaHgAAAA%3D%3D&amp;lang=zh_CN&amp;pass_ticket=Pez2L9XDcBocZbYtTKYpsk%2FR4fSYfpeveg4kCSebHQ%2Fc%2Ff7okp600GJYfrr0O7jo&amp;wx_header=1" target="_blank" rel="noopener">安装使用</a><br>lmdb<a href="https://blog.csdn.net/qq_41185868/article/details/90294583" target="_blank" rel="noopener">Py之lmdb：lmdb的简介、安装、使用方法之详细攻略</a></p>
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        <div class="show-content">
          <p>首先进入链接:<a href="https://notebooks.azure.com/" target="_blank">微软</a><a href="https://notebooks.azure.com/" target="_blank">https://notebooks.azure.com/</a></p><p>﻿</p><div class="image-package">
<img name="-48066696-1556241438878" src="http://upload-images.jianshu.io/upload_images/17163699-874256ee4418f149.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50"><br><div class="image-caption">点击右上角菜单栏</div>
</div><p>﻿</p><div class="image-package">
<img name="901312892-1556241461907" src="http://upload-images.jianshu.io/upload_images/17163699-219a0aa0664a7a98.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50"><br><div class="image-caption">登录你的微软账号(免费注册一个吧)</div>
</div><p>﻿</p><div class="image-package">
<img name="-1564159303-1556241511046" src="http://upload-images.jianshu.io/upload_images/17163699-60e92778cbe07a61.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50"><br><div class="image-caption">增加一个项目(理解成创建新python文件）</div>
</div><p>﻿</p><div class="image-package">
<img name="-766237159-1556241551549" src="http://upload-images.jianshu.io/upload_images/17163699-2c2b73b066cd3021.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50"><br><div class="image-caption">给你的新文件起个名字吧，ID随便填一下吧</div>
</div><p>﻿</p><div class="image-package">
<img name="-709902378-1556241590060" src="http://upload-images.jianshu.io/upload_images/17163699-526706446d8a9bf4.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50"><br><div class="image-caption">让我们开始吧</div>
</div><p>﻿</p><div class="image-package">
<img name="63084699-1556241616602" src="http://upload-images.jianshu.io/upload_images/17163699-3f32f3beb992baf1.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50"><br><div class="image-caption">你也可以选择R等其他编程语言</div>
</div><p>﻿</p><div class="image-package">
<img name="1836304579-1556241641389" src="http://upload-images.jianshu.io/upload_images/17163699-e9324bee6802b2b3.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1080/q/50"><br><div class="image-caption">很快很强大</div>
</div><p><a href="https://www.jianshu.com/p/901e0a6a7a8f?utm_campaign=haruki&amp;utm_content=note&amp;utm_medium=reader_share&amp;utm_source=weixin_timeline&amp;from=timeline" target="_blank">电脑版攻略看这里</a></p><p>If you are interested in this topic.﻿<br></p><p>You can get in touch with me.﻿</p><p>18234056952(Tel  wechat  qq)</p>
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